Identifying disease associations via integration between single cell methylomics and genome wide association studies
By Xinzhe Li
Identifying disease associations via integration between single cell methylomics and genome wide association studies
A project in our lab investigated the single cell disease risk association in brain single cell methylomics in human. We identified widespread brain specific disease signal in major depression disorder, schizophrenia, bipolar disorders as well as other brain related traits such as education and social well-being.
Our method allowed for single cell resolution disease risk investigation with methylation by integrating single cell methylomics with genome wide association studies (GWAS). Because of the single cell resolution, we were able to identify specific origins of different types of…
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Identifying causal paths from SNP to chromatin to gene expression (Pathfinder)
By Robert Smith, Megan Roytman
A paper recently published in our lab by Roytman et al. "Methods for fine-mapping with chromatin and expression data" presents a fine-mapping framework which computes posterior probabilities for causal paths from SNP to gene expression through chromatin.
The basis of this hypothesis was drawn from knowledge that a significant amount of SNPs in regulatory regions of the genome are simultaneously associated with histone modifications (changes in chromatin) and gene expression.
Pathfinder takes as input SNP-chromatin mark and chromatin mark-expression association statistics, as well as correlations on both the SNP and mark levels. Through simulations, this paper demonstrates that…
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Insights from ‘zooming in’ to look at Local Genetic Correlation using Summary Statistics (ρ-HESS)
By Robert Smith, Huwenbo Shi, and Nick Mancuso
Rho-HESS, a method recently developed in our lab by Shi et al. 2017 (Local genetic correlation gives insights into the shared genetic architecture of complex traits), quantifies the correlation between pairs of traits due to genetic variation at a small region in the genome. For example, height and BMI are correlated complex traits–by ‘zooming in’ to relevant genomic sections this method can identify individual loci affecting both traits.
Similar studies have previously looked at specific genomic regions using individual-level DNA. In 2015, we saw the development of a method called “Cross Trait LD Score Regression” by Bulik-Sullivan, Finucane…
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Tips for Formatting A Lot of GWAS Summary Association Statistics Data
By Huwenbo Shi
February 2nd, 2018, see original post @Huwenbo
Summary
Publicly available GWAS summary association statistics data are in all kinds of formats. The diversity of data formats is often attributable to the nature of the phenotypes being studied (e.g. case-control trait / quantitative traits) and the software used to perform the analysis. However, before any post-GWAS analyses, one needs to convert data in various formats into the same format. This page aims to provide some tips, guidelines, and protocols that I find useful for formatting a lot of GWAS summary statistics data to help prevent pitfalls in post-GWAS analyses.
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Visualizing fine-mapping studies with CANVIS
By Ruth Johnson
January 25th, 2018, see the original post @Ruth
One of the first projects I worked on was a fine-mapping visualization tool called CANVIS (Correlation ANnotation Visualization). The motivation behind CANVIS is to address the current patchwork-like method of visualizing a fine-mapping locus.
Recent fine-mapping methods such as PAINTOR (Kichaev et al. 2014) make use of summary statistics, LD, and functional annotations to compute posterior probabilities of a SNP to be causal. Although there is current software to visualize a locus, the underlying LD structure, and the resulting posterior probabilities, it still remains a hassle to string all of these individual…
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